def test_run_inference_auto(self, resnet_8_batch_model_downloader,
input_data_downloader_v1_224,
start_server_batch_model_auto,
create_channel_for_batching_server_auto):
print("Downloaded model files:", resnet_8_batch_model_downloader)
# Connect to grpc service
stub = create_channel_for_batching_server_auto
batch_input = input_data_downloader_v1_224[:6, :, :, :]
out_name = 'resnet_v1_50/predictions/Reshape_1'
output = infer_batch(batch_input=batch_input,
input_tensor='input',
grpc_stub=stub,
model_spec_name='resnet',
model_spec_version=None,
output_tensors=[out_name])
print("output shape", output[out_name].shape)
assert output[out_name].shape == (6, 1000), ERROR_SHAPE
batch_input = input_data_downloader_v1_224[:1, :, :, :]
out_name = 'resnet_v1_50/predictions/Reshape_1'
output = infer_batch(batch_input=batch_input,
input_tensor='input',
grpc_stub=stub,
model_spec_name='resnet',
model_spec_version=None,
output_tensors=[out_name])
print("output shape", output[out_name].shape)
assert output[out_name].shape == (1, 1000), ERROR_SHAPE
def test_run_inference(self, resnet_8_batch_model_downloader,
input_data_downloader_v1_224,
start_server_batch_model,
create_channel_for_batching_server):
"""
<b>Description</b>
Submit request to gRPC interface serving a single resnet model
<b>input data</b>
- directory with the model in IR format
- docker image with ie-serving-py service
- input data in numpy format
<b>fixtures used</b>
- model downloader
- input data downloader
- service launching
<b>Expected results</b>
- response contains proper numpy shape
"""
print("Downloaded model files:", resnet_8_batch_model_downloader)
# Starting docker with ie-serving
result = start_server_batch_model
print("docker starting status:", result)
time.sleep(40) # Waiting for inference service to load models
assert result == 0, "docker container was not started successfully"
# Connect to grpc service
stub = create_channel_for_batching_server
batch_input = input_data_downloader_v1_224[:8, :, :, :]
out_name = 'resnet_v1_50/predictions/Reshape_1'
output = infer_batch(batch_input=batch_input,
input_tensor='input',
grpc_stub=stub,
model_spec_name='resnet',
model_spec_version=None,
output_tensors=[out_name])
print("output shape", output[out_name].shape)
assert output[out_name].shape == (8, 1000), ERROR_SHAPE
def test_run_inference(self, download_two_models,
input_data_downloader_v1_224,
input_data_downloader_v3_331,
start_server_multi_model,
create_channel_for_port_multi_server):
"""
<b>Description</b>
Execute inference request using gRPC interface hosting multiple models
<b>input data</b>
- directory with 2 models in IR format
- docker image
- input data in numpy format
<b>fixtures used</b>
- model downloader
- input data downloader
- service launching
<b>Expected results</b>
- response contains proper numpy shape for both models set in config
file: model resnet_v1_50, pnasnet_large
- both served models handles appropriate input formats
"""
print("Downloaded model files:", download_two_models)
# Starting docker with ie-serving
result = start_server_multi_model
print("docker starting multi model server status:", result)
time.sleep(30) # Waiting for inference service to load models
assert result == 0, "docker container was not started successfully"
# Connect to grpc service
stub = create_channel_for_port_multi_server
input_data = input_data_downloader_v1_224[:2, :, :, :]
print("Starting inference using resnet model")
out_name = 'resnet_v1_50/predictions/Reshape_1'
for x in range(0, 10):
output = infer_batch(input_data, input_tensor='input',
grpc_stub=stub,
model_spec_name='resnet_V1_50',
model_spec_version=None,
output_tensors=[out_name])
print("output shape", output[out_name].shape)
assert output[out_name].shape == (2, 1000), ERROR_SHAPE
imgs_v1_224 = np.array(input_data_downloader_v1_224)
out_name = 'resnet_v1_50/predictions/Reshape_1'
for x in range(0, 10):
output = infer(imgs_v1_224, slice_number=x,
input_tensor='input', grpc_stub=stub,
model_spec_name='resnet_gs',
model_spec_version=None,
output_tensors=[out_name])
print("output shape", output[out_name].shape)
assert output[out_name].shape == (1, 1000), ERROR_SHAPE
out_name = 'resnet_v1_50/predictions/Reshape_1'
for x in range(0, 10):
output = infer(imgs_v1_224, slice_number=x,
input_tensor='input', grpc_stub=stub,
model_spec_name='resnet_s3',
model_spec_version=None,
output_tensors=[out_name])
print("output shape", output[out_name].shape)
assert output[out_name].shape == (1, 1000), ERROR_SHAPE
input_data = input_data_downloader_v3_331[:4, :, :, :]
print("Starting inference using pnasnet_large model")
out_name = 'final_layer/predictions'
for x in range(0, 10):
output = infer_batch(input_data, input_tensor='input',
grpc_stub=stub,
model_spec_name='pnasnet_large',
model_spec_version=None,
output_tensors=[out_name])
print("output shape", output[out_name].shape)
assert output[out_name].shape == (4, 1001), ERROR_SHAPE
